In the fast-moving world of banking, delivering excellent customer service has become a critical factor for businesses to stand out. As with any other industry, Banks face the challenge of providing prompt and precise responses to their customers' inquiries. However, with the growing number of customer queries, maintaining high levels of productivity while offering exceptional service can be difficult. This is where Paradigm's advanced language model capabilities can be beneficial in optimizing customer service productivity in the banking industry.
Experience a new world
With the help of AI, an insured or advisor can ask a question in natural language
and get a response in just a few seconds.
User:
"How can I dispute a bank transaction carried out using my credit card?"
Prompting:
Transmitting user input to generate the prompt
Context:
Transmitting contextual informations (eg: user ID, contract ID)
Searching
A search engine performs a search for the most relevant content.
Embedding
The embedding process is applied to the content selected by the search engine in order to compare it with the prompt.
Chatbot:
"You can dispute a banking transaction carried out via your credit card by contacting your bank in writing and presenting the ticket issued by the TPE or proof of the payment order to which the dispute relates. You can also contact the credit card issuer in writing, attaching the receipt or proof of the payment order."
Source Conditions-generales-particuliers.PDF page 29, 34 and 39
Improved autonomy
Increased user satisfaction
Time-saving
Cost savings
2 sec
Average response time to user inquiries.
78%
Rate of inquiries resolved without human intervention.
90%
Customer satisfaction ratings.
-80%
Time savings achieved through automation.
Prompting involves providing a hint or suggestion to the language model to generate more accurate and relevant responses.
Fine-tuning refers to the process of training a pre-trained language model on a specific dataset to improve its performance on a specific task.
Embedding is a process of representing text data in numerical format that a machine learning model can understand and process.
LightOn adheres to strict data privacy regulations and ensures that customer data is stored and processed securely in a private cloud or on-premises environment.